The high prevalence of drug addiction is a major health challenge that pressures healthcare systems to respond with cost-effective treatments. To improve the treatment success of drug-dependent patients, it is necessary to identify the main associated risk factors for dropping out of treatment. Previous research shows disparate results due to the wide variety of approaches employed, the different and/or poorly defined metrics used, and the different target populations under study. This paper presents the design and selection of a predictive model to estimate success of inpatient cocaine treatment based on a high-dimensional heterogeneous set of characteristics, with the aim of learning new associations between independent characteristics. We evaluated different feature selection techniques and machine learning algorithms to design the best predictive model in terms of accuracy, area under the receiver operating characteristic curve, recall, specificity, F1-score, and Matthews correlation coefficient. Random Forest was the top-performing model with a characteristic set consisting of 11 features selected with a wrapper evaluator and the Best First algorithm, achieving 82% accuracy, 0.81 of area under the receiver operating characteristic curve, 0.96 of recall, 0.47 of specificity, 0.89 of F1-measure and 0.53 of Matthews correlation coefficient. The predictive model's performance was enhanced by combining multiple dimensions with variables referring to previous treatments, mental exploration, cognitive functioning, personality, consumption habits, and pharmacological treatment. We have refined the use of machine learning techniques to predict drug addiction treatment success, which could represent a new step in treatment management especially when included in clinical decision support systems.

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